Evolutionary virus immune strategy for temporal networks based on community vitality

Preventing viruses spreading in networks is a hot topic. Existing immune strategies are mainly designed for static networks, which become ineffective for temporal networks. In this paper, we propose an evolutionary virus immune strategy for temporal networks, which takes into account the community evolution. First, we define a new metric, community vitality (CV), to quantize the evolution characteristics of communities. Second, based on the community vitality, we propose an immune strategy which selects an optimized number of initial nodes according to node influence (NI). Third, a theoretical analysis is proposed to measure the immune effect of the evolutionary immune strategy. Compared with the random immunization, the targeted immunization and the acquaintance immune strategy, we show that the proposed strategy has a much larger coverage, i.e., more nodes will have immune ability given the same number of initial immune nodes.